Background: Immunization information systems (IISs) are electronic registries used to monitor individual vaccination status and assess vaccine coverage. IISs are currently not widely used across Canada, where health jurisdictions employ a range of approaches to capture influenza immunization information. Conducted in advance of the 2009 H1N1 vaccination campaign, the objectives of this study were to understand the perceived value of individual-level data and IISs for influenza control, identify ideal system functions, and explore barriers to implementation.
Methods: In July and August 2009, semi-structured interviews were conducted with key informants engaged in vaccine delivery and/or pandemic planning at regional, provincial/territorial and federal levels across Canada. Key informants were recruited using a combination of convenience and snowball sampling methodologies. Qualitative analysis was used to extract themes from interview content.
Results: Patient management, assessment of vaccine coverage, and evaluation of safety and effectiveness were identified as public health priorities that would be achieved in a more timely manner, and with greater accuracy, through the use of an IIS. Features described as ideal included system flexibility, rapid data entry, and universality. Financial and human resource constraints as well as coordination between immunization providers were expressed as barriers to implementation.
Conclusions: IISs were perceived as valuable by key informants for strengthening management capacity and improving evaluation of both seasonal and pandemic influenza vaccination campaigns. However, certain implementation restrictions may need to be overcome for these benefits to be achieved.
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http://dx.doi.org/10.1186/1471-2458-10-523 | DOI Listing |
Med Phys
December 2024
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
Background: In clinical practices, doctors usually need to synthesize several single-modality medical images for diagnosis, which is a time-consuming and costly process. With this background, multimodal medical image fusion (MMIF) techniques have emerged to synthesize medical images of different modalities, providing a comprehensive and objective interpretation of the lesion.
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PLoS One
December 2024
Hangzhou Institute of Medicine (HIM), Zhejiang Cancer Hospital, Zhejiang, Hangzhou, China.
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View Article and Find Full Text PDFPLoS One
December 2024
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning Province, China.
The iterative shrinkage-thresholding algorithm (ISTA) is a classic optimization algorithm for solving ill-posed linear inverse problems. Recently, this algorithm has continued to improve, and the iterative weighted shrinkage-thresholding algorithm (IWSTA) is one of the improved versions with a more evident advantage over the ISTA. It processes features with different weights, making different features have different contributions.
View Article and Find Full Text PDFJ Adv Nurs
December 2024
School of Nursing, Jilin University, Changchun, Jilin, People's Republic of China.
Background: In a response to the lack of public awareness of dementia, 'ageing in place' and 'dementia-friendly community' policies have been proposed, and a number of relevant studies have been carried out.
Aim: To map the evidence of public help for people with dementia in the community.
Design: A scoping review.
PLoS One
December 2024
School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.
Background: Worldwide, health systems have been challenged by the overwhelming demands of the COVID-19 pandemic. In Ethiopia, maintaining essential health services during the COVID-19 pandemic is critical to preventing severe outcomes and protecting the gains made over the past years in the health sector. This project aims to explore the health system's response to maintaining essential healthcare services in Addis Ababa, Ethiopia.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!